Lung cancer remains the leading cause of cancer deaths in the U.S., and definitive radiotherapy is the standard of care for locally advanced, inoperable cases. Radiation therapy is also highly flexible, and can be adapted for variations in tumor volume and location. However, decision support models to optimize treatment planning and avoid morbidity on a patient-by-patient basis are lacking. Recently, the randomized phase III trial, RTOG 0617, compared high dose (74 Gy) treatments to standard (60 Gy) treatments for non-small cell lung cancer (NSCLC), and demonstrated an unexpectedly higher rate of mortality in the high-dose arm. We hypothesize that this was primarily due to heart irradiation, in combination with lung irradiation factors. The goal of this project is to determine and validate predictive models of morbidity following thoracic RT, in order to achieve safer and more optimal tradeoffs between local control and morbidity. We will jointly analyze three large, high-quality datasets using innovative image-registration, machine learning, and dose characteristics (dose-based radiomics), to develop mathematical models that could be used to predict and avoid key morbidity endpoints in radiotherapy treatment planning. All endpoints will use dose-distribution comparison methods to examine the correlation between dose and morbidity as a function of anatomic location, thus providing greater assurance that the source of radiotherapy toxicity is correctly identified. Under Specific Aim 1 (SA1), we will use innovative deformable image registration methods to map all patient dose distributions to a segmented reference case anatomy (including heart, lungs, bronchii, and blood vessels). The accuracy of the deformations will be mapped carefully using an innovative non-parametric estimation methodology. Tissue regions with high correlations to morbidity will be extracted for machine learning under SAs 2 and 3. Under SA2, we will apply innovative machine learning with the goal of relating dose factors extracted under SA1 with treatment-related death hazard. Under SA3, we will apply the same methodology to the risk of severe pneumonitis. We hypothesize that treatment related death hazard and radiation pneumonitis form a single predictable continuum of increasing risk, and we will attempt to bring together the results of SA2 and SA3 in a single predictive model. Innovative post-modeling analyses of mutually exclusive predictors will be used to make the machine learning models interpretable. The overall goals of this grant are: (1) to advance the state of science in the field of personally-optimized radiation oncology, and (2) to provide decision support tools to guide patient-specific tradeoffs between local control and toxicity in lung cancer radiotherapy.